【自动化任务流水线案例分析】

from celery import Celery
app = Celery('tasks', broker='redis://localhost:6379/0')

@app.task
def process_data(data):
    # 数据处理逻辑
    return transformed_data

from airflow import DAG
from airflow.operators.python_operator import PythonOperator

def extract():
    # 数据提取逻辑
    pass

dag = DAG('pipeline', schedule_interval='@daily')
extract_task = PythonOperator(task_id='extract', python_callable=extract, dag=dag)

import redis
r = redis.Redis()
for job in job_list:
    r.lpush('job_queue', job.serialize())

while True:
    job_data = r.brpop('job_queue')[1]
    process_job(job_data)

import time
from random import random

def retry_task(func, max_retries=3):
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            wait_time = 2 ** attempt + random()
            time.sleep(wait_time)
    raise Exception("Max retries exceeded")

from prometheus_client import Counter, start_http_server

processed_jobs = Counter('jobs_processed', 'Total processed jobs')
start_http_server(8000)

def run_job():
    processed_jobs.inc()
    # 任务逻辑

import structlog
logger = structlog.get_logger()

def process_item(item):
    logger.info("processing_item", item_id=item.id)

from multiprocessing import Pool

def parallel_process(items):
    with Pool(4) as p:
        return p.map(process_item, items)